There are some concepts clashing here.

I mean, if you let the LLM build a testris bot, it would be 1000x better than what the LLMs are doing. So yes, it is fun to win against an AI, but to be fair against such processing power, you should not be able to win. It is only possible because LLMs are not built for such tasks.

Task: play tetris

Task: write and optimize a tetris bot

Task: write and safely online optimize a tetris bot with consideration for cost to converge

openai/baselines (7 years ago) was leading on RL and then AlphaZero and Self-Attention Transformer networks.

LLMs are trained with RL, but aren't general purpose game theoretic RL agents?

"Optimizing Tetris Gameplay Using Reinforcement Learning Framework with Adaptive Genetic Algorithms" (2025) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5906702 .. https://scholar.google.com/scholar?cites=1615762352187216859...

"Outsmarting algorithms: A comparative battle between Reinforcement Learning and heuristics in Atari Tetris" (2025) https://dl.acm.org/doi/10.1016/j.eswa.2025.127251

Fun fact: Humans were not build for playing Tetris either!

Don't let Alan Kay[1] read that...

[1]: https://news.ycombinator.com/user?id=alankay

> By scaling up model parameters and leveraging substantial computational resources

So, how large is that new model?

While Qwen2.5 was pre-trained on 18 trillion tokens, Qwen3 uses nearly twice that amount, with approximately 36 trillion tokens covering 119 languages and dialects.

https://qwen.ai/blog?id=qwen3

Thanks for the info, but I don't think it answers the question. I mean, you could train a 20-node network on 36 trillion tokens. Wouldn't make much sense, but you could. So I was asking more about the number of nodes / parameters or GB of file size.

In addition, there seem to be many different versions of Qwen3. E.g. here the list from ollama library: https://ollama.com/library/qwen3/tags

This is the Max series models with unreleased weights, so probably larger than the largest released one. Also when refering to models, use huggingface or modelscope (wherever it is published) ollama is a really poor source on model info. they have some some bad naming (like confusing people on the deepseek R1 models), renaming, and more on model names, and they default to q4 quants, witch is a good sweet-spot but really degrades performance compared to the raw weigths.

There is certainly some truth to this, but why does it have to be black-and-white?

Nobody forces you to completely let go of the code and do pure vibe coding. You can also do small iterations.

It is one thing to do that while you have that boss, but something completely different to keep acting that way even when you have a different boss. The more people you have on a team who keep their mouths shut, the less effective it will be.

> forking is easy, sustaining is hard.

That is exactly the point. But it makes sense if you look at it from the other side. When you put in the effort to maintain a project, there have to be boundaries to the social interactions, and when those are reached, "just fork it" is a pressure valve to protect the ones who put in the effort to maintain projects.

Many people think they know how something should be done better, but as a community, we have to protect the ones who are not just talking, but actually maintaining.

So, how much does the galaxy's travel affect the speed of time?

I actually ran the numbers on time dilation! At 600km/s (0.2%), the effect is surprisingly small. We basically 'save' about 63 seconds a year compared to a stationary observer relative to the CMB. Not enough to live forever, but enough to be late for a meeting.

Pretty cool, thank you :-)

From a syntax perspective, I prefer the component syntax in Vue / Riot, which is HTML-like. That way, the general structure is clear, and you have to learn only the additional directives. As a bonus, syntax highlighting in most editors just works without an additional plugin.

I think it depends on size. If the icon is very small, I like the simple ones. If the icon is large, I like the detailed ones. Optimally, you can have an icon with more detailed versions when displayed larger, but it remains the same icon.

While I love the Laptops, I still wonder why the 'Precision Wireless Travel Mouse' has the attribute 'Precision' in its name.

I never owned another mouse as laggy and imprecise. Its design is good, but its basic mouse functionality is just very bad.

Also, this is the most basic two-button mouse design, the same as all cheapo mice since the 90s, what is so innovative about it?

A few positives from the design:

- You can easily remove the upper part which is hold by magnets - It is very easy to clean that way - You can store the receiver inside the mouse, very handy for transport - It runs on AA batteries, which hold for a while, and you can easily replace them if you need to

So the design definitely has some positives, but it isn't worth much if the mouse is laggy and imprecise (independent of the surface you use it on). And I am not talking about games or other real-time stuff, just too laggy for office work.